Search

Are human rights a single unit of an Indivisible or diminishing among those fundamental rights the right to adequate housing. And the right to adequate housing is one of the fundamental rights of shelter, or the void, which prevents the human from external factors and provides the needs necessary. The tender on the right to adequate housing changes, many are no longer limited to the ceiling and wall but also to the appropriate of the social and cultural location and the need for continuing basic services and personality and the nature of its inhabitants. The Caltech international charters for Human Rights paid great attention to the right to adequate housing after an essential part of human rights, it is no longer the human right to adequate housing is a topic or a personal right but it really became a collective of internationally hosted by the international legislation and international bodies involved in human rights and can have the right to adequate housing anyway. that means the minimum you can to ensure the survival, it is a prerequisite for Operations different case to a particular group and cross relationships. And promote the right to adequate housing and the importance of great owing to his enjoyment of economic, social and cultural rights and other rights are interrelated with each other. And can be seen to the human right to adequate housing in isolation from the fundamental rights of man is linked to the right of man to freely choose his residence and shall not be subject Lai arbitrary interference in his privacy or his home and his correspondence. The right man in the right to adequate housing has to be state intervention and the international community, the most important recognition of states and governments on the rights to adequate housing and protect and strengthen the provision of basic services for the right of water, electricity and etc. And the human right to adequate housing is the right to enjoy what is more than a roof and four walls it is the right of every man, woman and child in getting to the safe house and a local community that belongs to it, or live in peace and dignity.

One of the most important activities in data science is defining a membership function in fuzzy system. Although there are few ways to describe membership function like artificial neural networks, genetic algorithms etc.; they are very complex and time consuming. On the other hand, the presence of outlier in a data set produces deceptive results in the modeling. So it is important to detect and eliminate them to prevent their negative effect on the modeling. This paper describes a new and simple way of constructing fuzzy membership function by using five-number summary of a data set. Five states membership function can be created in this new method. At the same time, if there is any outlier in the data set, it can be detected with the help of this method. Usually box plot is used to identify the outliers of a data set. So along with the new approach, the box plot has also been drawn so that it is understood that the results obtained in the new method are accurate. Several real life examples and their analysis have been discussed with graph to demonstrate the potential of the proposed method. The results obtained show that the proposed method has given good results. In the case of outlier, the proposed method and the box plot method have shown similar results. Primary advantage of this new procedure is that it is not as expensive as neural networks, and genetic algorithms.

Mobile networks are composed of many base stations and for each of them many parameters must be optimized to provide good services. Automatically and dynamically optimizing all these entities is challenging as they are sensitive to variations in the environment and can affect each other through interferences. Reinforcement learning (RL) algorithms are good candidates to automatically learn base station configuration strategies from incoming data but they are often hard to scale to many agents. In this work, we demonstrate how to use coordination graphs and reinforcement learning in a complex application involving hundreds of cooperating agents. We show how mobile networks can be modeled using coordination graphs and how network optimization problems can be solved efficiently using multi- agent reinforcement learning. The graph structure occurs naturally from expert knowledge about the network and allows to explicitly learn coordinating behaviors between the antennas through edge value functions represented by neural networks. We show empirically that coordinated reinforcement learning outperforms other methods. The use of local RL updates and parameter sharing can handle a large number of agents without sacrificing coordination which makes it well suited to optimize the ever denser networks brought by 5G and beyond., 14 pages, 8 figures

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better QoS

Different individuals use different coping styles to cope with their problems. In patients with anxiety and/or depression, these have important implications. The primary objective of our study was to estimate the frequency of different coping mechanisms used by patients with symptoms of anxiety and depression. A descriptive, cross-sectional survey was conducted and patients with symptoms of anxiety and depression were identified using the Aga Khan University’s Anxiety and Depression Scale (AKUADS). Coping styles were determined by using the 28-item Brief COPE inventory. We were able to recruit 162 people. The prevalence of anxiety and depression was found to be 34%. Females were more than 2 times likely to have anxiety and depression (P value , ). In patients screening positive for AKUADS, “religion” was the most common coping mechanism identified. “Acceptance”, “Use of instrumental support”, and “Active coping” were other commonly used coping styles. Our findings suggest that religious coping is a common behavior in patients presenting with symptoms anxiety and depression in Pakistan. Knowledge of these coping styles is important in the care of such patients, as these coping methods can be identified and to some extent modified by the treating clinician/psychiatrist.

Robustness of embedded biometric systems is of prime importance with the emergence of fourth generation communication devices and advancement in security systems This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional image space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Principal Component Analysis and Lindear Discriminant Analysis with K-Nearest Neighbor and implementing such system in real-time using SignalWAVE.

The growing debate regarding long working hours of postgraduate trainees has been receiving considerable attention recently. This greater workload contributes to increasing stress. Our objective was to specifically study the association between long working hours, stress and the greater use of 'maladaptive' coping strategies.A cross-sectional descriptive study was carried out on all interns and residents at the Aga Khan University Hospital during February to May, 2005. Level of stress was measured by use of General Health Questionnaire (GHQ-12) and the use of maladaptive coping mechanisms through Brief Cope-28.55.1% scored over the threshold for mild stress i.e. GHQ3, while more than 46% of the trainees scored over the threshold of more than 4 for morbid stress. Trainees under stress reported more working hours on average as compared to those not under stress, 83.8 and 74.7 hours respectively. At the same time, those working for longer hours were more likely to have used these negative coping mechanisms, which would further contribute to more stress rather than relieving it.Significant levels of stress have been identified. Along with this, those working for longer hours were more likely to have used these negative coping mechanisms. Reduction of working hours is important. Simultaneously, interventions need to be planned at imparting knowledge, awareness and skills to cope with various kinds of stressors encountered by a trainee during his/her training. Additionally, limits need to be devised for the working hours of the trainees.